Noisy Natural Gradient as Variational Inference

6 December 2017

Papers citing "Noisy Natural Gradient as Variational Inference"

43 / 43 papers shown

Title
Spectral-factorized Positive-definite Curvature Learning for NN Training Wu Lin Felix Dangel Runa Eschenhagen Juhan Bae Richard E. Turner Roger B. Grosse 47 0 0 10 Feb 2025
Manifold Sampling for Differentiable Uncertainty in Radiance Fields Linjie Lyu Ayush Tewari Marc Habermann Shunsuke Saito Michael Zollhöfer Thomas Leimkühler Christian Theobalt UQCV 38 1 0 19 Sep 2024
Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani Marvin Pfortner Tobias Weber Philipp Hennig UQCV 60 1 0 07 Jun 2024
Scalable Bayesian Learning with posteriors Samuel Duffield Kaelan Donatella Johnathan Chiu Phoebe Klett Daniel Simpson BDL UQCV 62 1 0 31 May 2024
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference Siqi Kou Lei Gan Dequan Wang Chongxuan Li Zhijie Deng BDL DiffM 21 7 0 17 Oct 2023
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 33 1 0 12 Jul 2023
Prediction-Oriented Bayesian Active Learning Freddie Bickford-Smith Andreas Kirsch Sebastian Farquhar Y. Gal Adam Foster Tom Rainforth 27 28 0 17 Apr 2023
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space Michael Diao Krishnakumar Balasubramanian Sinho Chewi Adil Salim BDL 13 20 0 10 Apr 2023
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Qizhang Li Yiwen Guo W. Zuo Hao Chen AAML 27 35 0 10 Feb 2023
Hierarchically Structured Task-Agnostic Continual Learning Heinke Hihn Daniel A. Braun BDL CLL 19 8 0 14 Nov 2022
Manifold Gaussian Variational Bayes on the Precision Matrix M. Magris M. Shabani Alexandros Iosifidis 31 2 0 26 Oct 2022
On the optimization and pruning for Bayesian deep learning X. Ke Yanan Fan BDL UQCV 22 1 0 24 Oct 2022
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning Zhijie Deng Feng Zhou Jun Zhu BDL 44 19 0 23 Oct 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani Nicholas Kramer Runa Eschenhagen Lorenzo Rosasco Philipp Hennig UQCV BDL 13 9 0 02 Aug 2022
Laplacian Autoencoders for Learning Stochastic Representations M. Miani Frederik Warburg Pablo Moreno-Muñoz Nicke Skafte Detlefsen Søren Hauberg UQCV BDL SSL 28 10 0 30 Jun 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Alexander Immer Tycho F. A. van der Ouderaa Gunnar Rätsch Vincent Fortuin Mark van der Wilk BDL 24 44 0 22 Feb 2022
Gradient Descent on Neurons and its Link to Approximate Second-Order Optimization Frederik Benzing ODL 35 23 0 28 Jan 2022
A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications Xinlei Zhou Han Liu Farhad Pourpanah T. Zeng Xizhao Wang UQCV UD 19 58 0 03 Nov 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 55 73 0 09 Jul 2021
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 30 1,109 0 07 Jul 2021
Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 13 15 0 18 Jun 2021
Data augmentation in Bayesian neural networks and the cold posterior effect Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison BDL 19 37 0 10 Jun 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 29 124 0 14 May 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li D. Duvenaud BDL UQCV 16 46 0 12 Feb 2021
The Hidden Uncertainty in a Neural Networks Activations Janis Postels Hermann Blum Yannick Strümpler César Cadena Roland Siegwart Luc Van Gool Federico Tombari UQCV 22 22 0 05 Dec 2020
Generalized Variational Continual Learning Noel Loo S. Swaroop Richard E. Turner BDL CLL 28 58 0 24 Nov 2020
A Trace-restricted Kronecker-Factored Approximation to Natural Gradient Kai-Xin Gao Xiaolei Liu Zheng-Hai Huang Min Wang Zidong Wang Dachuan Xu F. Yu 19 11 0 21 Nov 2020
Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians Juhan Bae Roger C. Grosse 11 24 0 26 Oct 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information Form Jongseo Lee Matthias Humt Jianxiang Feng Rudolph Triebel BDL UQCV 25 46 0 20 Jun 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 13 60 0 17 May 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 11 277 0 24 Feb 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule Wu Lin Mark W. Schmidt Mohammad Emtiyaz Khan BDL 29 35 0 24 Feb 2020
Being Bayesian about Categorical Probability Taejong Joo U. Chung Minji Seo UQCV BDL 19 58 0 19 Feb 2020
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation Janis Postels Francesco Ferroni Huseyin Coskun Nassir Navab Federico Tombari UQCV UD PER BDL 27 139 0 01 Aug 2019
The Functional Neural Process Christos Louizos Xiahan Shi Klamer Schutte Max Welling BDL 11 77 0 19 Jun 2019
Stochastic Neural Network with Kronecker Flow Chin-Wei Huang Ahmed Touati Pascal Vincent Gintare Karolina Dziugaite Alexandre Lacoste Aaron Courville BDL 19 8 0 10 Jun 2019
Practical Deep Learning with Bayesian Principles Kazuki Osawa S. Swaroop Anirudh Jain Runa Eschenhagen Richard E. Turner Rio Yokota Mohammad Emtiyaz Khan BDL UQCV 41 240 0 06 Jun 2019
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner Lukas Balles Philipp Hennig 17 207 0 29 May 2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi Sébastien Marmin Maurizio Filippone BDL 19 14 0 27 May 2019
An Empirical Study of Large-Batch Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen Kevin Luk Maxime Gazeau Guodong Zhang Harris Chan Jimmy Ba ODL 15 22 0 21 Feb 2019
Good Initializations of Variational Bayes for Deep Models Simone Rossi Pietro Michiardi Maurizio Filippone BDL 17 21 0 18 Oct 2018
Variational Optimization J. Staines David Barber DRL 57 53 0 18 Dec 2012
MCMC using Hamiltonian dynamics Radford M. Neal 182 3,262 0 09 Jun 2012